NDLI: SQL-MapReduce hybrid approach towards distributed projected clustering

Content Provider	IEEE Xplore Digital Library
Author	Harikumar, S. Shyju, M. Kaimal, M.R.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Amrita Vishwa Vidyapeetham, Kollam, India (Harikumar, S.; Shyju, M.; Kaimal, M.R.)
Abstract	Clustering high dimensional data is a major challenge in data mining due to the existence of inherent complexity and sparsity of the data. Projected clustering is one of the clustering approaches that determine the clusters in the subspaces of such high dimensional data. However, projected clustering within DBMS is quite computationally expensive in time and space complexity, when the volume of records is in terms of terabytes, petabytes and more. This expensive computation becomes a hurdle especially when the data clustering on transactional data is used as a preprocessing step for other tasks such as frequent decision making, efficient indexing, compression, etc. Hence, parallelizing and distributing expensive data clustering tasks becomes attractive in terms of speed-up of computation and the increased amount of memory available in a computing cluster. Inorder to achieve this, we propose a SQL-MapReduce hybrid approach for scalable projected clustering.
Starting Page	18
Ending Page	23
File Size	6299164
Page Count	6
File Format	PDF
ISBN	9781479968701
e-ISBN	9781479954605
DOI	10.1109/ICDSE.2014.6974605
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Data analysis Parallel computing Hive Merging Clustering algorithms Distributed databases Relational databases Hadoop Distributed computing Projected clustering MapReduce
Content Type	Text
Resource Type	Article

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